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Course Outline
Introduction to AI in Quality Control
- Overview of AI applications in manufacturing quality processes
- Applications in inspection, defect detection, and regulatory compliance
- Benefits and limitations of AI-driven QA
Collecting and Preparing Quality Data
- Types of data utilized in QA (images, sensors, production logs)
- Labeling visual datasets using LabelImg
- Data storage and structural organization for model training
Introduction to Computer Vision for QA
- Fundamentals of image processing with OpenCV
- Preprocessing techniques tailored for industrial images
- Extracting visual features for detailed analysis
Machine Learning for Anomaly Detection
- Training simple classifiers for defect detection
- Utilizing convolutional neural networks (CNNs)
- Unsupervised learning techniques for anomaly identification
Yield Forecasting with AI Models
- Introduction to regression techniques
- Developing models to forecast production yields
- Evaluating and enhancing prediction accuracy
Integrating AI with Production Systems
- Deployment options for inspection models
- Edge AI versus cloud-based analysis
- Automating alerts and quality reporting mechanisms
Practical Case Study and Final Project
- Developing an end-to-end AI inspection prototype
- Training and testing with sample QA datasets
- Presenting a functional quality control AI solution
Summary and Next Steps
Requirements
- Foundational knowledge of basic manufacturing or QA processes
- Familiarity with spreadsheets or digital reporting tools
- A keen interest in data-driven quality control methodologies
Target Audience
- Quality assurance specialists
- Production team leaders
21 Hours
